{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Riskfolio-Lib Tutorial: \n",
    "<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__\n",
    "<br>__[Orenji](https://www.orenj-i.net)__\n",
    "<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__\n",
    "<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__\n",
    "<a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> \n",
    "\n",
    "## Tutorial 6: Portfolio Optimization with Custom Parameters\n",
    "\n",
    "## 1. Downloading the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  8 of 8 completed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "yf.pdr_override()\n",
    "pd.options.display.float_format = '{:.4%}'.format\n",
    "\n",
    "# Date range\n",
    "start = '2016-01-01'\n",
    "end = '2019-12-30'\n",
    "\n",
    "# Tickers of assets\n",
    "assets =  ['IEUR', 'IPAC', 'IVV', 'EWC', 'SCZ', 'IJR', 'XCS.TO', 'EIMI.L']\n",
    "assets.sort()\n",
    "\n",
    "# Downloading data\n",
    "data = yf.download(assets, start = start, end = end)\n",
    "data = data.loc[:,('Adj Close', slice(None))]\n",
    "data.columns = assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EIMI.L</th>\n",
       "      <th>EWC</th>\n",
       "      <th>IEUR</th>\n",
       "      <th>IJR</th>\n",
       "      <th>IPAC</th>\n",
       "      <th>IVV</th>\n",
       "      <th>SCZ</th>\n",
       "      <th>XCS.TO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>0.5865%</td>\n",
       "      <td>-0.4719%</td>\n",
       "      <td>-0.6498%</td>\n",
       "      <td>0.3069%</td>\n",
       "      <td>0.5109%</td>\n",
       "      <td>0.2079%</td>\n",
       "      <td>-0.2627%</td>\n",
       "      <td>-0.2423%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-0.9404%</td>\n",
       "      <td>-2.1811%</td>\n",
       "      <td>-1.7442%</td>\n",
       "      <td>-1.1402%</td>\n",
       "      <td>-1.9695%</td>\n",
       "      <td>-1.3091%</td>\n",
       "      <td>-1.6818%</td>\n",
       "      <td>-0.6478%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-1.6328%</td>\n",
       "      <td>-2.3752%</td>\n",
       "      <td>-1.6741%</td>\n",
       "      <td>-2.5694%</td>\n",
       "      <td>-2.0955%</td>\n",
       "      <td>-2.3928%</td>\n",
       "      <td>-1.5252%</td>\n",
       "      <td>-2.4450%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>-1.8722%</td>\n",
       "      <td>-0.4469%</td>\n",
       "      <td>-1.0556%</td>\n",
       "      <td>-1.6651%</td>\n",
       "      <td>-1.4784%</td>\n",
       "      <td>-1.1077%</td>\n",
       "      <td>-1.3395%</td>\n",
       "      <td>0.0835%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>-0.8065%</td>\n",
       "      <td>-1.3466%</td>\n",
       "      <td>0.1521%</td>\n",
       "      <td>0.0098%</td>\n",
       "      <td>0.1792%</td>\n",
       "      <td>0.1037%</td>\n",
       "      <td>0.1697%</td>\n",
       "      <td>-3.6728%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             EIMI.L      EWC     IEUR      IJR     IPAC      IVV      SCZ  \\\n",
       "Date                                                                        \n",
       "2016-01-05  0.5865% -0.4719% -0.6498%  0.3069%  0.5109%  0.2079% -0.2627%   \n",
       "2016-01-06 -0.9404% -2.1811% -1.7442% -1.1402% -1.9695% -1.3091% -1.6818%   \n",
       "2016-01-07 -1.6328% -2.3752% -1.6741% -2.5694% -2.0955% -2.3928% -1.5252%   \n",
       "2016-01-08 -1.8722% -0.4469% -1.0556% -1.6651% -1.4784% -1.1077% -1.3395%   \n",
       "2016-01-11 -0.8065% -1.3466%  0.1521%  0.0098%  0.1792%  0.1037%  0.1697%   \n",
       "\n",
       "             XCS.TO  \n",
       "Date                 \n",
       "2016-01-05 -0.2423%  \n",
       "2016-01-06 -0.6478%  \n",
       "2016-01-07 -2.4450%  \n",
       "2016-01-08  0.0835%  \n",
       "2016-01-11 -3.6728%  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating returns\n",
    "\n",
    "Y = data[assets].pct_change().dropna()\n",
    "\n",
    "display(Y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Estimating Mean Variance Portfolios with Custom Parameters\n",
    "\n",
    "### 2.1 Calculating the portfolio that maximizes Sharpe ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EIMI.L</th>\n",
       "      <th>EWC</th>\n",
       "      <th>IEUR</th>\n",
       "      <th>IJR</th>\n",
       "      <th>IPAC</th>\n",
       "      <th>IVV</th>\n",
       "      <th>SCZ</th>\n",
       "      <th>XCS.TO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>1.0741%</td>\n",
       "      <td>16.4312%</td>\n",
       "      <td>44.3799%</td>\n",
       "      <td>8.0854%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>30.0294%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         EIMI.L      EWC     IEUR     IJR    IPAC     IVV     SCZ   XCS.TO\n",
       "weights 1.0741% 16.4312% 44.3799% 8.0854% 0.0000% 0.0000% 0.0000% 30.0294%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.Portfolio as pf\n",
    "\n",
    "# Building the portfolio object\n",
    "port = pf.Portfolio(returns=Y)\n",
    "# Calculating optimum portfolio\n",
    "\n",
    "# Load our custom estimates of input parameters\n",
    "custom_mu = pd.read_excel('custom_posterior_mu.xlsx', engine='openpyxl', index_col=0).T\n",
    "custom_cov = pd.read_excel('custom_posterior_cov.xlsx', engine='openpyxl', index_col=0)\n",
    "\n",
    "# Input manually the custom parameters:\n",
    "port.mu = custom_mu / 100 # Custom mean vector.\n",
    "port.cov = custom_cov / 100 # Custom covariance matrix.\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "model='Classic' # Could be Classic (historical), BL (Black Litterman) or FM (Factor Model)\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe\n",
    "hist = True # Use historical scenarios for risk measures that depend on scenarios\n",
    "rf = 0 # Risk free rate\n",
    "l = 0 # Risk aversion factor, only useful when obj is 'Utility'\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.PlotFunctions as plf\n",
    "\n",
    "# Plotting the composition of the portfolio\n",
    "\n",
    "ax = plf.plot_pie(w=w, title='Sharpe Mean Variance', others=0.05, nrow=25, cmap = \"tab20\",\n",
    "                  height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EIMI.L</th>\n",
       "      <th>EWC</th>\n",
       "      <th>IEUR</th>\n",
       "      <th>IJR</th>\n",
       "      <th>IPAC</th>\n",
       "      <th>IVV</th>\n",
       "      <th>SCZ</th>\n",
       "      <th>XCS.TO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>41.5844%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>58.4156%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>36.3916%</td>\n",
       "      <td>7.7363%</td>\n",
       "      <td>0.5369%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>55.3351%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>33.5377%</td>\n",
       "      <td>13.0756%</td>\n",
       "      <td>1.6955%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>51.6912%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>31.1222%</td>\n",
       "      <td>17.6117%</td>\n",
       "      <td>2.6649%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>48.6012%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>28.9760%</td>\n",
       "      <td>21.6434%</td>\n",
       "      <td>3.5230%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>45.8577%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EIMI.L      EWC     IEUR     IJR    IPAC     IVV     SCZ   XCS.TO\n",
       "0 0.0000% 41.5844%  0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 58.4156%\n",
       "1 0.0000% 36.3916%  7.7363% 0.5369% 0.0000% 0.0000% 0.0000% 55.3351%\n",
       "2 0.0000% 33.5377% 13.0756% 1.6955% 0.0000% 0.0000% 0.0000% 51.6912%\n",
       "3 0.0000% 31.1222% 17.6117% 2.6649% 0.0000% 0.0000% 0.0000% 48.6012%\n",
       "4 0.0000% 28.9760% 21.6434% 3.5230% 0.0000% 0.0000% 0.0000% 45.8577%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 50 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the efficient frontier\n",
    "\n",
    "label = 'Max Risk Adjusted Return Portfolio' # Title of point\n",
    "mu = port.mu # Expected returns\n",
    "cov = port.cov # Covariance matrix\n",
    "returns = port.returns # Returns of the assets\n",
    "\n",
    "ax = plf.plot_frontier(w_frontier=frontier, mu=mu, cov=cov, returns=returns, rm=rm,\n",
    "                       rf=rf, alpha=0.05, cmap='viridis', w=w, label=label,\n",
    "                       marker='*', s=16, c='r', height=6, width=10, t_factor=252, ax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting efficient frontier composition\n",
    "\n",
    "ax = plf.plot_frontier_area(w_frontier=frontier, cmap=\"tab20\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Estimating Mean Risk Portfolios with Custom Mean Parameter\n",
    "\n",
    "In this part I will calculate optimal portfolios for several risk measures using a custom mean parameter as input. First I'm going to calculate the portfolio that maximizes risk adjusted return when CVaR is the risk measure, then I'm going to calculate the portfolios that maximize the risk adjusted return for all available risk measures.\n",
    "\n",
    "### 3.1 Calculating the portfolio that maximizes Return/CVaR ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EIMI.L</th>\n",
       "      <th>EWC</th>\n",
       "      <th>IEUR</th>\n",
       "      <th>IJR</th>\n",
       "      <th>IPAC</th>\n",
       "      <th>IVV</th>\n",
       "      <th>SCZ</th>\n",
       "      <th>XCS.TO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>20.4696%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>69.1583%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.3721%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          EIMI.L     EWC    IEUR     IJR    IPAC      IVV     SCZ   XCS.TO\n",
       "weights 20.4696% 0.0000% 0.0000% 0.0000% 0.0000% 69.1583% 0.0000% 10.3721%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rm = 'CVaR' # Risk measure\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plf.plot_pie(w=w, title='Sharpe Mean CVaR', others=0.05, nrow=25, cmap = \"tab20\",\n",
    "                  height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EIMI.L</th>\n",
       "      <th>EWC</th>\n",
       "      <th>IEUR</th>\n",
       "      <th>IJR</th>\n",
       "      <th>IPAC</th>\n",
       "      <th>IVV</th>\n",
       "      <th>SCZ</th>\n",
       "      <th>XCS.TO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.7534%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>27.0454%</td>\n",
       "      <td>5.9547%</td>\n",
       "      <td>7.7069%</td>\n",
       "      <td>40.5397%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.3813%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.9974%</td>\n",
       "      <td>20.6484%</td>\n",
       "      <td>10.6227%</td>\n",
       "      <td>35.3503%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.8883%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.0542%</td>\n",
       "      <td>26.7811%</td>\n",
       "      <td>11.3386%</td>\n",
       "      <td>32.9378%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.7094%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.0572%</td>\n",
       "      <td>31.2599%</td>\n",
       "      <td>10.9022%</td>\n",
       "      <td>31.0713%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.8520%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.1646%</td>\n",
       "      <td>34.3678%</td>\n",
       "      <td>10.9970%</td>\n",
       "      <td>29.6186%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    EIMI.L     EWC    IEUR     IJR     IPAC      IVV      SCZ   XCS.TO\n",
       "0 18.7534% 0.0000% 0.0000% 0.0000% 27.0454%  5.9547%  7.7069% 40.5397%\n",
       "1 22.3813% 0.0000% 0.0000% 0.0000% 10.9974% 20.6484% 10.6227% 35.3503%\n",
       "2 23.8883% 0.0000% 0.0000% 0.0000%  5.0542% 26.7811% 11.3386% 32.9378%\n",
       "3 23.7094% 0.0000% 0.0000% 0.0000%  3.0572% 31.2599% 10.9022% 31.0713%\n",
       "4 23.8520% 0.0000% 0.0000% 0.0000%  1.1646% 34.3678% 10.9970% 29.6186%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 50 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'Max Risk Adjusted Return Portfolio' # Title of point\n",
    "\n",
    "ax = plf.plot_frontier(w_frontier=frontier, mu=mu, cov=cov, returns=returns, rm=rm,\n",
    "                       rf=rf, alpha=0.05, cmap='viridis', w=w, label=label,\n",
    "                       marker='*', s=16, c='r', height=6, width=10, t_factor=252, ax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting efficient frontier composition\n",
    "\n",
    "ax = plf.plot_frontier_area(w_frontier=frontier, cmap=\"tab20\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Calculate Optimal Portfolios for Several Risk Measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk Measures available:\n",
    "#\n",
    "# 'MV': Standard Deviation.\n",
    "# 'MAD': Mean Absolute Deviation.\n",
    "# 'MSV': Semi Standard Deviation.\n",
    "# 'FLPM': First Lower Partial Moment (Omega Ratio).\n",
    "# 'SLPM': Second Lower Partial Moment (Sortino Ratio).\n",
    "# 'CVaR': Conditional Value at Risk.\n",
    "# 'EVaR': Entropic Value at Risk.\n",
    "# 'WR': Worst Realization (Minimax)\n",
    "# 'MDD': Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio).\n",
    "# 'ADD': Average Drawdown of uncompounded cumulative returns.\n",
    "# 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'UCI': Ulcer Index of uncompounded cumulative returns.\n",
    "\n",
    "rms = ['MV', 'MAD', 'MSV', 'FLPM', 'SLPM', 'CVaR',\n",
    "       'EVaR', 'WR', 'MDD', 'ADD', 'CDaR', 'UCI', 'EDaR']\n",
    "\n",
    "w_s = pd.DataFrame([])\n",
    "\n",
    "for i in rms:\n",
    "    w = port.optimization(model=model, rm=i, obj=obj, rf=rf, l=l, hist=hist)\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "            background-color:  #92d183;\n",
       "            color:  #000000;\n",
       "        }#T_60599_row7_col7{\n",
       "            background-color:  #006335;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_60599_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MV</th>        <th class=\"col_heading level0 col1\" >MAD</th>        <th class=\"col_heading level0 col2\" >MSV</th>        <th class=\"col_heading level0 col3\" >FLPM</th>        <th class=\"col_heading level0 col4\" >SLPM</th>        <th class=\"col_heading level0 col5\" >CVaR</th>        <th class=\"col_heading level0 col6\" >EVaR</th>        <th class=\"col_heading level0 col7\" >WR</th>        <th class=\"col_heading level0 col8\" >MDD</th>        <th class=\"col_heading level0 col9\" >ADD</th>        <th class=\"col_heading level0 col10\" >CDaR</th>        <th class=\"col_heading level0 col11\" >UCI</th>        <th class=\"col_heading level0 col12\" >EDaR</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_60599_level0_row0\" class=\"row_heading level0 row0\" >EIMI.L</th>\n",
       "                        <td id=\"T_60599_row0_col0\" class=\"data row0 col0\" >1.07%</td>\n",
       "                        <td id=\"T_60599_row0_col1\" class=\"data row0 col1\" >2.85%</td>\n",
       "                        <td id=\"T_60599_row0_col2\" class=\"data row0 col2\" >12.04%</td>\n",
       "                        <td id=\"T_60599_row0_col3\" class=\"data row0 col3\" >0.33%</td>\n",
       "                        <td id=\"T_60599_row0_col4\" class=\"data row0 col4\" >13.04%</td>\n",
       "                        <td id=\"T_60599_row0_col5\" class=\"data row0 col5\" >20.47%</td>\n",
       "                        <td id=\"T_60599_row0_col6\" class=\"data row0 col6\" >18.06%</td>\n",
       "                        <td id=\"T_60599_row0_col7\" class=\"data row0 col7\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row0_col8\" class=\"data row0 col8\" >34.89%</td>\n",
       "                        <td id=\"T_60599_row0_col9\" class=\"data row0 col9\" >4.62%</td>\n",
       "                        <td id=\"T_60599_row0_col10\" class=\"data row0 col10\" >34.89%</td>\n",
       "                        <td id=\"T_60599_row0_col11\" class=\"data row0 col11\" >14.66%</td>\n",
       "                        <td id=\"T_60599_row0_col12\" class=\"data row0 col12\" >34.28%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_60599_level0_row1\" class=\"row_heading level0 row1\" >EWC</th>\n",
       "                        <td id=\"T_60599_row1_col0\" class=\"data row1 col0\" >16.43%</td>\n",
       "                        <td id=\"T_60599_row1_col1\" class=\"data row1 col1\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row1_col2\" class=\"data row1 col2\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row1_col3\" class=\"data row1 col3\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row1_col4\" class=\"data row1 col4\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row1_col5\" class=\"data row1 col5\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row1_col6\" class=\"data row1 col6\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row1_col7\" class=\"data row1 col7\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row1_col8\" class=\"data row1 col8\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row1_col9\" class=\"data row1 col9\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row1_col10\" class=\"data row1 col10\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row1_col11\" class=\"data row1 col11\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row1_col12\" class=\"data row1 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_60599_level0_row2\" class=\"row_heading level0 row2\" >IEUR</th>\n",
       "                        <td id=\"T_60599_row2_col0\" class=\"data row2 col0\" >44.38%</td>\n",
       "                        <td id=\"T_60599_row2_col1\" class=\"data row2 col1\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row2_col2\" class=\"data row2 col2\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row2_col3\" class=\"data row2 col3\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row2_col4\" class=\"data row2 col4\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row2_col5\" class=\"data row2 col5\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row2_col6\" class=\"data row2 col6\" >9.10%</td>\n",
       "                        <td id=\"T_60599_row2_col7\" class=\"data row2 col7\" >9.02%</td>\n",
       "                        <td id=\"T_60599_row2_col8\" class=\"data row2 col8\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row2_col9\" class=\"data row2 col9\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row2_col10\" class=\"data row2 col10\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row2_col11\" class=\"data row2 col11\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row2_col12\" class=\"data row2 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_60599_level0_row3\" class=\"row_heading level0 row3\" >IJR</th>\n",
       "                        <td id=\"T_60599_row3_col0\" class=\"data row3 col0\" >8.09%</td>\n",
       "                        <td id=\"T_60599_row3_col1\" class=\"data row3 col1\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row3_col2\" class=\"data row3 col2\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row3_col3\" class=\"data row3 col3\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row3_col4\" class=\"data row3 col4\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row3_col5\" class=\"data row3 col5\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row3_col6\" class=\"data row3 col6\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row3_col7\" class=\"data row3 col7\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row3_col8\" class=\"data row3 col8\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row3_col9\" class=\"data row3 col9\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row3_col10\" class=\"data row3 col10\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row3_col11\" class=\"data row3 col11\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row3_col12\" class=\"data row3 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_60599_level0_row4\" class=\"row_heading level0 row4\" >IPAC</th>\n",
       "                        <td id=\"T_60599_row4_col0\" class=\"data row4 col0\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row4_col1\" class=\"data row4 col1\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row4_col2\" class=\"data row4 col2\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row4_col3\" class=\"data row4 col3\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row4_col4\" class=\"data row4 col4\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row4_col5\" class=\"data row4 col5\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row4_col6\" class=\"data row4 col6\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row4_col7\" class=\"data row4 col7\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row4_col8\" class=\"data row4 col8\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row4_col9\" class=\"data row4 col9\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row4_col10\" class=\"data row4 col10\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row4_col11\" class=\"data row4 col11\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row4_col12\" class=\"data row4 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_60599_level0_row5\" class=\"row_heading level0 row5\" >IVV</th>\n",
       "                        <td id=\"T_60599_row5_col0\" class=\"data row5 col0\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row5_col1\" class=\"data row5 col1\" >96.64%</td>\n",
       "                        <td id=\"T_60599_row5_col2\" class=\"data row5 col2\" >83.64%</td>\n",
       "                        <td id=\"T_60599_row5_col3\" class=\"data row5 col3\" >99.56%</td>\n",
       "                        <td id=\"T_60599_row5_col4\" class=\"data row5 col4\" >82.20%</td>\n",
       "                        <td id=\"T_60599_row5_col5\" class=\"data row5 col5\" >69.16%</td>\n",
       "                        <td id=\"T_60599_row5_col6\" class=\"data row5 col6\" >50.55%</td>\n",
       "                        <td id=\"T_60599_row5_col7\" class=\"data row5 col7\" >47.54%</td>\n",
       "                        <td id=\"T_60599_row5_col8\" class=\"data row5 col8\" >65.11%</td>\n",
       "                        <td id=\"T_60599_row5_col9\" class=\"data row5 col9\" >95.38%</td>\n",
       "                        <td id=\"T_60599_row5_col10\" class=\"data row5 col10\" >65.11%</td>\n",
       "                        <td id=\"T_60599_row5_col11\" class=\"data row5 col11\" >85.34%</td>\n",
       "                        <td id=\"T_60599_row5_col12\" class=\"data row5 col12\" >65.72%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_60599_level0_row6\" class=\"row_heading level0 row6\" >SCZ</th>\n",
       "                        <td id=\"T_60599_row6_col0\" class=\"data row6 col0\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row6_col1\" class=\"data row6 col1\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row6_col2\" class=\"data row6 col2\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row6_col3\" class=\"data row6 col3\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row6_col4\" class=\"data row6 col4\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row6_col5\" class=\"data row6 col5\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row6_col6\" class=\"data row6 col6\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row6_col7\" class=\"data row6 col7\" >1.08%</td>\n",
       "                        <td id=\"T_60599_row6_col8\" class=\"data row6 col8\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row6_col9\" class=\"data row6 col9\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row6_col10\" class=\"data row6 col10\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row6_col11\" class=\"data row6 col11\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row6_col12\" class=\"data row6 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_60599_level0_row7\" class=\"row_heading level0 row7\" >XCS.TO</th>\n",
       "                        <td id=\"T_60599_row7_col0\" class=\"data row7 col0\" >30.03%</td>\n",
       "                        <td id=\"T_60599_row7_col1\" class=\"data row7 col1\" >0.50%</td>\n",
       "                        <td id=\"T_60599_row7_col2\" class=\"data row7 col2\" >4.31%</td>\n",
       "                        <td id=\"T_60599_row7_col3\" class=\"data row7 col3\" >0.11%</td>\n",
       "                        <td id=\"T_60599_row7_col4\" class=\"data row7 col4\" >4.76%</td>\n",
       "                        <td id=\"T_60599_row7_col5\" class=\"data row7 col5\" >10.37%</td>\n",
       "                        <td id=\"T_60599_row7_col6\" class=\"data row7 col6\" >22.29%</td>\n",
       "                        <td id=\"T_60599_row7_col7\" class=\"data row7 col7\" >42.36%</td>\n",
       "                        <td id=\"T_60599_row7_col8\" class=\"data row7 col8\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row7_col9\" class=\"data row7 col9\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row7_col10\" class=\"data row7 col10\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row7_col11\" class=\"data row7 col11\" >0.00%</td>\n",
       "                        <td id=\"T_60599_row7_col12\" class=\"data row7 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7faea115c730>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
